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This is the seventh article in a series about Agent Experience (AX): the practice of making AI coding agents work correctly with your technology. The series covers what you can and can't control in the agent stack, how to measure whether your extensions are helping or hurting, and how to iterate toward better outcomes. …
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Python's agent-framework-orchestrations package is now 1.0.0. That puts Microsoft Agent Framework's orchestration layer at 1.0 across Python and .NET. Sequential, concurrent, group chat, handoff, and magentic orchestration are now stable in both SDKs. You can pick the coordination pattern that fits your problem instead …
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Agentic coding benchmarks are getting closer to real-world software development. For Kotlin teams, the most important question is how reliably AI agents can complete end-to-end Kotlin tasks, from reading an issue to producing a solution that passes validation. We’re taking the first step in addressing that gap by …
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Model Context Protocol explained for C# and .NET developers: what MCP is, why it matters, and how hosts, clients, servers, tools, resources, and prompts fit. Link to article: https://www.devleader.ca/2026/07/08/model-context-protocol-mcp-explained-for-c-and-net-developers
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Set up a private NuGet feed with Azure Artifacts or GitHub Packages. Learn nuget.config setup, authentication, and consuming private packages in .NET. Link to article: https://www.devleader.ca/2026/07/07/private-nuget-feeds-in-net-azure-artifacts-and-github-packages
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Object detection powers transformative applications, from autonomous vehicles navigating city streets and security systems identifying threats in real time to retail analytics tracking inventory and medical imaging detecting tumors. But choosing the right model for your computer vision project can be challenging, …
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In this post, we walk through what Dataset Enrichment is, how it differs from legacy Topics, and provide three migration scenarios with step-by-step guidance so you can move your business context into the dataset layer with confidence. Link to article: …
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Today, we are excited to announce Multi-Dataset Relationships in Amazon Quick Sight. This new capability lets you define logical relationships between Quick Sight datasets and perform runtime joins at query time. Instead of flattening tables ahead of time, you keep each table as its own Quick Sight dataset and declare …
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In this post, we shift from concepts to patterns. For each schema, you’ll find a table structure, use cases, implementation steps, and sample SQL queries. We also cover workarounds for advanced scenarios that require extra modeling steps, and close with a summary of current limitations. Link to article: …
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This post is for data architects, business intelligence (BI) engineers, and analytics engineers building or optimizing Quick Sight Topics for natural-language Chat-based exploration. Link to article: https://aws.amazon.com/blogs/machine-learning/multi-dataset-topic-best-practices-for-amazon-quick-chat/
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In this post, we walk through how multi-dataset Topics work, explain how the chat agent uses defined relationships to generate cross-dataset queries, and demonstrate an end-to-end implementation using a retail analytics scenario in Quick Sight. Link to article: …
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This post walks through building a serverless image editor where users upload a photo, describe an edit in plain English, and receive the result in seconds. The agent runs on AgentCore harness without custom orchestration code. We deploy the full solution, including authentication, encrypted storage, three image …
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Implementing a data and model monitoring solution is necessary to maintain prediction accuracy and help achieve the best outcome for your machine learning use case. This post shows how you can use open source Evidently together with Amazon SageMaker AI to generate monitoring reports, organize and compare the results in …
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In this post, you build an AWS Support Companion using Amazon Bedrock AgentCore. The agent uses Strands Agents as the orchestration framework and connects to AWS services through the Model Context Protocol (MCP). By the end, you have a working agent that can analyze CloudWatch logs, search AWS documentation, query …
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In this post, we show how AWS Finance used chat agents and Flows in Amazin Quick to transform two of their most time-consuming workflows. Link to article: https://aws.amazon.com/blogs/machine-learning/how-aws-finance-teams-reclaimed-hundreds-of-hours-with-amazon-quick/
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Applications are not packages. Let me repeat. Applications are not packages. This is a common misconception due to the (too) often blurring of concepts and terminology. Packages and applications are different and distinct things, so it's important to understand what those words mean (to MSIX). Packages and A Link to …
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A little while ago, I mentioned that Windows 95 had some defenses against installers that overwrite a file with an older version. These defenses kicked in whenever it dete Link to article: https://devblogs.microsoft.com/oldnewthing/20260707-00/?p=112508
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There's advice making the rounds: replace your CLI args with a single --json payload so agents can use your tool more effectively. The thinking being, that agents already think in structured formats, and nested data maps cleanly to JSON. Flat args on the other hand, force awkward conventions like repeating …
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What we’re building toward As the share of code written by AI agents grows rapidly, two questions are central to effective AI adoption: How can teams turn AI usage into real productivity gains, and how can they maintain quality, security, and control while doing so? CI/CD is critical to both. Every contribution, …
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In any recent model comparison, you’ve probably seen a single number from a coding benchmark to represent “How good this model is at coding”. Those scores are tempting: they’re simple, they’re leaderboard-friendly, and they seem to tell a clear story about progress. But that story didn’t hold up when we looked more …
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